The spatial semantic hierarchy
Artificial Intelligence
A Theory of the Quasi-Static World
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Automatic Learning of an Activity-Based Semantic Scene Model
AVSS '03 Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance
Intelligent data analysis
Statistical Background Subtraction for a Mobile Observer
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Using Interaction Signatures to Find and Label Chairs and Floors
IEEE Pervasive Computing
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Non-Local Algorithm for Image Denoising
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Bayesian Modeling of Dynamic Scenes for Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modelling Scenes Using the Activity within Them
Proceedings of the international conference on Spatial Cognition VI: Learning, Reasoning, and Talking about Space
Scene modeling and change detection in dynamic scenes: A subspace approach
Computer Vision and Image Understanding
Categorizing Perceptions of Indoor Rooms Using 3D Features
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Moving Object Segmentation Using Optical Flow and Depth Information
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
Robust Multiperson Tracking from a Mobile Platform
IEEE Transactions on Pattern Analysis and Machine Intelligence
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Learning kinematic models for articulated objects
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Laser-based navigation enhanced with 3D time-of-flight data
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Real-time foreground-background segmentation using codebook model
Real-Time Imaging
Learning semantic scene models by trajectory analysis
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Estimating Object Proper Motion Using Optical Flow, Kinematics, and Depth Information
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Integrating multiple viewpoints for articulated scene model aquisition
ICVS'13 Proceedings of the 9th international conference on Computer Vision Systems
Hi-index | 0.00 |
In this paper we describe an efficient but detailed new approach to analyze complex dynamic scenes directly in 3D. The arising information is important for mobile robots to solve tasks in the area of household robotics. In our work a mobile robot builds an articulated scene model by observing the environment in the visual field or rather in the so-called vista space. The articulated scene model consists of essential knowledge about the static background, about autonomously moving entities like humans or robots and finally, in contrast to existing approaches, information about articulated parts. These parts describe movable objects like chairs, doors or other tangible entities, which could be moved by an agent. The combination of the static scene, the self-moving entities and the movable objects in one articulated scene model enhances the calculation of each single part. The reconstruction process for parts of the static scene benefits from removal of the dynamic parts and in turn, the moving parts can be extracted more easily through the knowledge about the background. In our experiments we show, that the system delivers simultaneously an accurate static background model, moving persons and movable objects. This information of the articulated scene model enables a mobile robot to detect and keep track of interaction partners, to navigate safely through the environment and finally, to strengthen the interaction with the user through the knowledge about the 3D articulated objects and 3D scene analysis.